An investigation into the classification of river environments using GIS : the case of KwaZulu-Natal rivers.

• Main Author: Sebake, Malete Daniel.
• Other Authors: Ahmed, Fethi B.
• Published: 2011
• Subjects: Rivers > KwaZulu-Natal > Classification.; River channels > KwaZulu-Natal.; Geomorphology.; Geographic information systems.; Theses > Environmental science.
• Online Access: http://hdl.handle.net/10413/3510

According to the National Water Act No. 36 of 1998 (DWAF 1999), classification of the water resources in South Africa is the initial step towards the implementation of protection or management programmes. This study reviews different methods and systems of classifying river environments, in order to recommend a convenient, efficient and flexible classification scheme for describing the conditions of river environments. To meet this challenge, the study proposes the use of Geographical Information System (GIS) as a tool to link different controlling variables of river environments and thereafter analyse their spatial relationships. The study supports the use of GIS in river environment classification, with special emphasis on its functionalities that make it possible to explore and manipulate data interactively and easily. The GIS allows the user a flexible way to analyse the geomorphologic and ecological controlling variables of the river system. Thus , it enables different arrangements of these controlling variables for a number of classification purposes. The methodology used involves the GIS (database) analyses and map analyses to explore the relationships between geomorphologic and ecological controlling-variables of the river ecosystem. A case study of the KwaZulu-Natal Rivers demonstrates that valuable information for river environments could be derived from available geomorphologic and ecological datasets using methods of analyses within the GIS. In addition, the case study illustrates that it is possible to develop a working classification scheme for a particular purpose. The developed classification scheme can be improved by considering influential factors such as, the use of up-to-date datasets, consistent projection parameters and relevant scale. === Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.